The ALife conferences are the major meeting of the artificial life research community since 1987. For its 15th edition in 2016, it was held in Latin America for the first time, in the Mayan Riviera, Mexico, from July 4 -8. The special them of the conference: How can the synthetic study of living systems contribute to societies: scientifically, technically, and culturally? The goal of the conference theme is to better understand societies with the purpose of using this understanding for a more efficient management and development of social systems.
Recent advances suggest that the concept of information might hold the key to unravelling the mystery of life's nature and origin. Fresh insights from a broad and authoritative range of articulate and respected experts focus on the transition from matter to life, and hence reconcile the deep conceptual schism between the way we describe physical and biological systems. A unique cross-disciplinary perspective, drawing on expertise from philosophy, biology, chemistry, physics, and cognitive and social sciences, provides a new way to look at the deepest questions of our existence. This book addresses the role of information in life, and how it can make a difference to what we know about the world. Students, researchers, and all those interested in what life is and how it began will gain insights into the nature of life and its origins that touch on nearly every domain of science. Hardcover: 514 pages; ISBN-10: 1107150531; ISBN-13: 978-1107150539;
I'm giving a talk at the Stanford Complexity Group this Thursday afternoon, April 20th. If you're around - like in Silicon Valley - please drop by! It will be in Clark S361 at 4 pm. Here's the idea. Everyone likes to say that biology is all about information. There's something true about this - just think about DNA. But what does this insight actually do for us? To figure it out, we need to do some work. Biology is also about things that can make copies of themselves. So it makes sense to figure out how information theory is connected to the 'replicator equation' — a simple model of population dynamics for self-replicating entities. To see the connection, we need to use relative information: the information of one probability distribution relative to another, also known as the Kullback–Leibler divergence. Then everything pops into sharp focus. It turns out that free energy — energy in forms that can actually be used, not just waste heat — is a special case of relative information Since the decrease of free energy is what drives chemical reactions, biochemistry is founded on relative information. But there's a lot more to it than this! Using relative information we can also see evolution as a learning process, fix the problems with Fisher's fundamental theorem of natural selection, and more. So this what I'll talk about! You can see slides of an old version here: http://math.ucr.edu/home/baez/bio_asu/ but my Stanford talk will be videotaped and it'll eventually be here: https://www.youtube.com/user/StanfordComplexity You can already see lots of cool talks at this location! #biology
Wondering if there’s a way I can manufacture a reason to head to Northern California this week…Syndicated copies to:
Anesthesia induces unconsciousness by changing the function of proteins that reside on the surface of a thin membrane that forms a barrier around all cells, according to new research from Weill Cornell Medicine scientists. The findings challenge a century-old concept of how anesthetics work and may help guide the development of new agents associated with fewer side effects.
One of the most important use cases of ontologies is the calculation of similarity scores between a query and items annotated with classes of an ontology. The hierarchical structure of an ontology does not necessarily reflect all relevant aspects of the domain it is modelling, and this can reduce the performance of ontology-based search algorithms. For instance, the classes of phenotype ontologies may be arranged according to anatomical criteria, but individual phenotypic features may affect anatomic entities in opposite ways. Thus, "opposite" classes may be located in close proximity in an ontology; for example enlarged liver and small liver are grouped under abnormal liver size. Using standard similarity measures, these would be scored as being similar, despite in fact being opposites. In this paper, we use information about opposite ontology classes to extend two large phenotype ontologies, the human and the mammalian phenotype ontology. We also show that this information can be used to improve rankings based on similarity measures that incorporate this information. In particular, cosine similarity based measures show large improvements. We hypothesize this is due to the natural embedding of opposite phenotypes in vector space. We support the idea that the expressivity of semantic web technologies should be explored more extensively in biomedical ontologies and that similarity measures should be extended to incorporate more than the pure graph structure defined by the subclass or part-of relationships of the underlying ontologies.
@lpachter Your cup of tea over at UCLA next week? Regulatory & Epigenetic Stochasticity in Development & Disease http://www.ipam.ucla.edu/programs/workshops/regulatory-and-epigenetic-stochasticity-in-development-and-disease
Pachter, a computational biologist, returns to CalTech to study the role and function of RNA.
Pachter, a computational biologist and Caltech alumnus, returns to the Institute to study the role and function of RNA.
Lior Pachter (BS ’94) is Caltech’s new Bren Professor of Computational Biology. Recently, he was elected a fellow of the International Society for Computational Biology, one of the highest honors in the field. We sat down with him to discuss the emerging field of applying computational methods to biology problems, the transition from mathematics to biology, and his return to Pasadena. Continue reading “👓 A Conversation with @LPachter (BS ’94) | Caltech”
The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process.
Epigenetics refers to information transmitted during cell division other than the DNA sequence per se, and it is the language that distinguishes stem cells from somatic cells, one organ from another, and even identical twins from each other. In contrast to the DNA sequence, the epigenome is relatively susceptible to modification by the environment as well as stochastic perturbations over time, adding to phenotypic diversity in the population. Despite its strong ties to the environment, epigenetics has never been well reconciled to evolutionary thinking, and in fact there is now strong evidence against the transmission of so-called “epi-alleles,” i.e. epigenetic modifications that pass through the germline.
However, genetic variants that regulate stochastic fluctuation of gene expression and phenotypes in the offspring appear to be transmitted as an epigenetic or even Lamarckian trait. Furthermore, even the normal process of cellular differentiation from a single cell to a complex organism is not understood well from a mathematical point of view. There is increasingly strong evidence that stem cells are highly heterogeneous and in fact stochasticity is necessary for pluripotency. This process appears to be tightly regulated through the epigenome in development. Moreover, in these biological contexts, “stochasticity” is hardly synonymous with “noise”, which often refers to variation which obscures a “true signal” (e.g., measurement error) or which is structural, as in physics (e.g., quantum noise). In contrast, “stochastic regulation” refers to purposeful, programmed variation; the fluctuations are random but there is no true signal to mask.
This workshop will serve as a forum for scientists and engineers with an interest in computational biology to explore the role of stochasticity in regulation, development and evolution, and its epigenetic basis. Just as thinking about stochasticity was transformative in physics and in some areas of biology, it promises to fundamentally transform modern genetics and help to explain phase transitions such as differentiation and cancer.
This workshop will include a poster session; a request for poster titles will be sent to registered participants in advance of the workshop.
Adam Arkin (Lawrence Berkeley Laboratory)
Gábor Balázsi (SUNY Stony Brook)
Domitilla Del Vecchio (Massachusetts Institute of Technology)
Michael Elowitz (California Institute of Technology)
Andrew Feinberg (Johns Hopkins University)
Don Geman (Johns Hopkins University)
Anita Göndör (Karolinska Institutet)
John Goutsias (Johns Hopkins University)
Garrett Jenkinson (Johns Hopkins University)
Andre Levchenko (Yale University)
Olgica Milenkovic (University of Illinois)
Johan Paulsson (Harvard University)
Leor Weinberger (University of California, San Francisco (UCSF))
Whether by virtue of being prepared in a slowly relaxing, high-free energy initial condition, or because they are constantly dissipating energy absorbed from a strong external drive, many systems subject to thermal fluctuations are not expected to behave in the way they would at thermal equilibrium. Rather, the probability of finding such a system in a given microscopic arrangement may deviate strongly from the Boltzmann distribution, raising the question of whether thermodynamics still has anything to tell us about which arrangements are the most likely to be observed. In this work, we build on past results governing nonequilibrium thermodynamics and define a generalized Helmholtz free energy that exactly delineates the various factors that quantitatively contribute to the relative probabilities of different outcomes in far-from-equilibrium stochastic dynamics. By applying this expression to the analysis of two examples—namely, a particle hopping in an oscillating energy landscape and a population composed of two types of exponentially growing self-replicators—we illustrate a simple relationship between outcome-likelihood and dissipative history. In closing, we discuss the possible relevance of such a thermodynamic principle for our understanding of self-organization in complex systems, paying particular attention to a possible analogy to the way evolutionary adaptations emerge in living things.
Notions like meaning, signal, intentionality, are difficult to relate to a physical word. I study a purely physical definition of "meaningful information", from which these notions can be derived. It is inspired by a model recently illustrated by Kolchinsky and Wolpert, and improves on Dretske classic work on the relation between knowledge and information. I discuss what makes a physical process into a "signal".
Understanding the emergence and robustness of life requires accounting for both chemical specificity and statistical generality. We argue that the reverse of a common observation—that life requires a source of free energy to persist—provides an appropriate principle to understand the emergence, organization, and persistence of life on earth. Life, and in particular core biochemistry, has many properties of a relaxation channel that was driven into existence by free energy stresses from the earth's geochemistry. Like lightning or convective storms, the carbon, nitrogen, and phosphorus fluxes through core anabolic pathways make sense as the order parameters in a phase transition from an abiotic to a living state of the geosphere. Interpreting core pathways as order parameters would both explain their stability over billions of years, and perhaps predict the uniqueness of specific optimal chemical pathways.
Driven by technological progress, human life expectancy has increased greatly since the nineteenth century. Demographic evidence has revealed an ongoing reduction in old-age mortality and a rise of the maximum age at death, which may gradually extend human longevity. Together with observations that lifespan in various animal species is flexible and can be increased by genetic or pharmaceutical intervention, these results have led to suggestions that longevity may not be subject to strict, species-specific genetic constraints. Here, by analysing global demographic data, we show that improvements in survival with age tend to decline after age 100, and that the age at death of the world’s oldest person has not increased since the 1990s. Our results strongly suggest that the maximum lifespan of humans is fixed and subject to natural constraints.
Almost 40 years ago, Leonard Hayflick discovered that cultured normal human cells have limited capacity to divide, after which they become senescent — a phenomenon now known as the ‘Hayflick limit’. Hayflick's findings were strongly challenged at the time, and continue to be questioned in a few circles, but his achievements have enabled others to make considerable progress towards understanding and manipulating the molecular mechanisms of ageing.
Biomolecular systems like molecular motors or pumps, transcription and translation machinery, and other enzymatic reactions, can be described as Markov processes on a suitable network. We show quite generally that, in a steady state, the dispersion of observables, like the number of consumed or produced molecules or the number of steps of a motor, is constrained by the thermodynamic cost of generating it. An uncertainty ε requires at least a cost of 2k_B T/ε^2 independent of the time required to generate the output.